In the present paper, we introduce the use of probabilistic neural network (PNN) that assumes a generative distribution for the target data [1]}. The PNN is able to estimate the expected value of the targets while also predicting a confidence interval for each estimation. For our presentation, we consider the construction of reduced-order models for fluid dynamics.
